SIA-GAN: Scrambling Inversion Attack Using Generative Adversarial Network

Koki Madono*, Masayuki Tanaka, Masaki Onishi, Tetsuji Ogawa

*この研究の対応する著者

研究成果: Article査読

抄録

This paper presents a scrambling inversion attack using a generative adversarial network (SIA-GAN). This method aims to evaluate the privacy protection level achieved by image scrambling method. For privacy-preserving machine learning, scrambled images are often used to protect visual information, assuming that searching the scramble parameters is highly difficult for an attacker due to the application of complex image scrambling operations. However, the security of such methods has not been thoroughly investigated. SIA-GAN learns the mapping between pairs of scrambled images and original images, then attempts to invert image scrambling. Therefore, the attacker is assumed to have real images whose domain is the same as that of scrambled images. Experimental results demonstrate that scrambled images cannot be recovered if block shuffling is applied as a scrambling operation. The experimental code of SIA-GAN is available at https://github.com/MADONOKOUKI/SIA-GAN.

本文言語English
ページ(範囲)129385-129393
ページ数9
ジャーナルIEEE Access
9
DOI
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)

フィンガープリント

「SIA-GAN: Scrambling Inversion Attack Using Generative Adversarial Network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル